Coal mill load scheduling optimization method, device and equipment and coal mill

Abstract

The embodiment of the invention discloses a coal mill load scheduling optimization method, device and equipment and a coal mill, and the method comprises the steps: taking the maximum value of the income capacity of a thermal power generating unit as a first optimization target, taking a peak regulation constraint condition as a constraint, and carrying out the solving through a preset optimization solver, and obtaining a fire coal instruction curve of the thermal power generating unit; and taking the maximum value of the low-load stable combustion capacity of the thermal power generating unit as a second optimization target, taking the output constraint condition as a constraint, taking the fire coal instruction curve as an input, and carrying out secondary solving by utilizing a preset optimization solver to obtain the optimal output distribution of the coal mill under deep peak regulation. The technical problems that the total power consumption is high and the economical efficiency is poor due to the fact that the coal mill operates in an equal output mode under deep peak regulation are solved, and the technical effects that the total power consumption of the coal mill under deep peak regulation is reduced, and the economical efficiency of deep peak regulation is improved are achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of coal mill control, and in particular to a method, device, equipment and coal mill for optimizing load scheduling of a coal mill. Background Art

[0002] At present, the change trends of the load and total coal supply of thermal power units are basically the same. There are peak sections, valley sections, rising sections and falling sections for both the unit load and coal supply. When the unit is deeply peaked, the load drops significantly. In order to reduce the operating cost, one of the coal mills can be switched from the operating state to the hot standby state. However, due to the different characteristics of each coal mill, the equal output method makes the high-efficiency coal mill unable to output as much as possible, resulting in high total power consumption of the coal mill and poor economic efficiency. Summary of the invention

[0003] The embodiments of the present invention provide a method, device, equipment and coal mill for optimizing load scheduling of a coal mill, which solve the technical problems of high total power consumption and poor economy when operating in an evenly distributed output mode under deep peak regulation.

[0004] An embodiment of the present invention provides a coal mill load scheduling optimization method, the optimization method comprising:

[0005] Obtain the profitability of thermal power units operating under deep peak load regulation;

[0006] Based on the operation conditions of the thermal power unit at different peak-shaving depths, determining the peak-shaving constraint conditions of the thermal power unit under deep peak-shaving operation, wherein the peak-shaving constraint conditions at least include deep power regulation and variable load rate;

[0007] Taking the maximum value of the profitability as the first optimization target, taking the peak load constraint condition as the constraint, solving by using a preset optimization solver, and obtaining the coal-fired command curve of the thermal power unit;

[0008] Determining the low-load stable combustion capability of the thermal power unit based on the boiler combustion condition of the thermal power unit;

[0009] Determine the output constraint conditions of the coal mill based on the safe operation conditions of the boiler;

[0010] The maximum value of the low-load stable combustion capability is taken as the second optimization target, the output constraint condition is taken as the constraint, the coal-fired command curve is taken as the input, and the preset optimization solver is used for secondary solution to obtain the optimal output distribution of the coal mill under deep peak regulation.

[0011] Furthermore, the profitability of thermal power units operating under deep peak load regulation includes:

[0012] Obtain the electricity loss income, peak load compensation income and energy consumption cost of thermal power units operating under deep peak load regulation;

[0013] The profit capacity of the thermal power unit under deep peak regulation is obtained by subtracting the energy consumption cost from the sum of the electricity loss income and the peak regulation compensation income.

[0014] Furthermore, based on the operation conditions of the thermal power unit at different peak load regulation depths, determining the peak load regulation constraint conditions of the thermal power unit under deep peak load regulation operation includes:

[0015] Determine the constraint condition of deep power regulation of the thermal power unit based on the output threshold value of the thermal power unit under conventional peak regulation and the minimum stable combustion limit output threshold value under the operation condition of deep peak regulation without oil injection;

[0016] The constraint conditions of the variable load rate of the thermal power unit are determined based on the automatic gain control performance of the thermal power unit under deep peak regulation.

[0017] Further, determining the low-load stable combustion capability of the thermal power unit based on the combustion condition of the boiler of the thermal power unit includes:

[0018] Determining a first calorific value of the lower burner based on the type of coal currently burning in the lower burner of the thermal power unit;

[0019] Determining the second calorific value of the pulverized coal combustion based on the relationship between the fluctuation amplitude of the furnace negative pressure of the thermal power unit and the load of the lower burner;

[0020] The first calorific value and the second calorific value are added together to obtain the low-load stable combustion capability of the thermal power unit.

[0021] Furthermore, the output constraint conditions of the coal mill determined based on the safe operation conditions of the boiler include:

[0022] Determine the output reduction amount and output increase amount of the coal mill based on the current operation status of the coal mill of the thermal power unit;

[0023] The output constraint condition of the coal mill is determined based on the rated output value of the coal mill, and the output reduction amount and the output increase amount of the coal mill.

[0024] Furthermore, the constraint conditions for determining the variable load rate of the thermal power unit based on the automatic gain control performance of the thermal power unit under deep peak regulation include:

[0025] Determining the unit type of the thermal power unit;

[0026] Determine the segmented adjustment rate that satisfies the automatic gain control performance for different types of units during deep peak regulation;

[0027] The segmented regulation rates corresponding to the different types of units are determined as constraints on the variable load rate.

[0028] Furthermore, the energy consumption cost includes at least: coal consumption cost, pollutant emission cost, low-load operation life loss cost, power plant fixed cost and production cost.

[0029] The embodiment of the present invention further provides a coal mill load scheduling optimization device, the optimization device comprising:

[0030] The revenue acquisition unit is used to obtain the revenue capacity of the thermal power unit under deep peak regulation;

[0031] A first condition determination unit is used to determine the peak-shaving constraint conditions of the thermal power unit under deep peak-shaving operation based on the operation conditions of the thermal power unit under different peak-shaving depths, wherein the peak-shaving constraint conditions at least include deep power regulation and variable load rate;

[0032] A first model solving unit is used to take the maximum value of the profitability as a first optimization target, take the peak load constraint condition as a constraint, and use a preset optimization solver to solve, so as to obtain a coal-fired command curve of the thermal power unit;

[0033] A stable combustion capability determination unit, configured to determine the low-load stable combustion capability of the thermal power unit based on the combustion condition of the boiler of the thermal power unit;

[0034] A second condition determination unit, used to determine the output constraint condition of the coal mill based on the boiler safety operation condition;

[0035] The second model solving unit is used to take the maximum value of the low-load stable combustion capability as the second optimization target, take the output constraint condition as the constraint, take the coal-fired command curve as the input, and use the preset optimization solver to perform a secondary solution to obtain the optimal output distribution of the coal mill under deep peak regulation.

[0036] The embodiment of the present invention further provides a coal mill load scheduling optimization device, the optimization device comprising:

[0037] at least one processor; and

[0038] a memory communicatively connected to the at least one processor; wherein,

[0039] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the coal mill load scheduling optimization method described in any of the above embodiments.

[0040] An embodiment of the present invention further provides a coal mill, which executes the coal mill load scheduling optimization method described in any of the above embodiments.

[0041] The embodiment of the present invention discloses a method, device, equipment and coal mill for optimizing load scheduling of coal mill, wherein the optimization method comprises: obtaining the profitability of a thermal power unit under deep peak regulation; determining the peak regulation constraint conditions of the thermal power unit under deep peak regulation based on the operation conditions of the thermal power unit under different peak regulation depths; taking the maximum value of profitability as the first optimization target, taking the peak regulation constraint conditions as the constraints, solving by using a preset optimization solver, and obtaining the coal-fired command curve of the thermal power unit; determining the low-load stable combustion capacity of the thermal power unit based on the boiler combustion conditions of the thermal power unit; determining the output constraint conditions of the coal mill based on the safe operation conditions of the boiler; taking the maximum value of the low-load stable combustion capacity as the second optimization target, taking the output constraint conditions as the constraints, taking the coal-fired command curve as the input, and performing a secondary solution by using a preset optimization solver, and obtaining the optimal output distribution of the coal mill under deep peak regulation. The present application solves the technical problem of high total power consumption and poor economy in the operation using an evenly distributed output mode under deep peak regulation, and achieves the technical effect of reducing the total power consumption of the coal mill under deep peak regulation and improving the economy of deep peak regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a structural diagram of a coal mill load scheduling optimization method provided by an embodiment of the present invention;

[0043] Figure 2 is a deep adjustment power limit diagram provided by an embodiment of the present invention;

[0044] Figure 3 It is a structural diagram of a coal mill load scheduling optimization device provided by the present invention;

[0045] Figure 4 It is a structural schematic diagram of a coal mill load scheduling optimization device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0047] It should be noted that the terms "first", "second", etc. in the specification, claims and drawings of the present invention are used to distinguish different objects, rather than to limit a specific order. The following embodiments of the present invention can be implemented separately, or in combination with each other, and the embodiments of the present invention do not impose specific limitations on this.

[0048] Figure 1 It is a structural diagram of a coal mill load scheduling optimization method provided by an embodiment of the present invention.

[0049] like Figure 1 As shown, the coal mill load scheduling optimization method specifically includes the following steps:

[0050] S101, obtain the profitability of thermal power units operating under deep peak regulation.

[0051] Specifically, the profitability of thermal power units under deep peak regulation is mainly composed of the following parts: electricity loss income, peak regulation compensation income and energy consumption cost.

[0052] Among them, when calculating the power loss income, since deep peak regulation is to reduce the load to reduce the operating cost, and the power generation will decrease when the load is reduced, when the planned power is determined, the reduction in power generation will result in a loss of part of the income, which is the power loss income, and is expressed as P qe,t The peak load compensation income refers to the fact that during the peak load phase, the electricity price will be compensated in stages based on the peak load depth, that is, it is slightly higher than the electricity price during the conventional power generation phase. This part of the income is the peak load compensation income, and is expressed as P cp,t Indicates; for energy consumption cost C ec,t Generally speaking, since coal-fired power plants are complex and large in size, and there are many factors that affect the cost, accurate calculation is very difficult. In simple terms, the overall operating cost of a power plant is usually calculated separately according to fixed cost and production cost. The above two parts of cost together constitute the energy consumption cost C. ec,t In addition, the energy cost C ec,t It also includes: coal consumption costs, pollutant emission costs, and low-load operation life loss costs, etc.

[0053] S102, based on the operation conditions of the thermal power units at different peak-shaving depths, determine the peak-shaving constraints of the thermal power units under deep peak-shaving operation, wherein the peak-shaving constraints at least include deep power regulation and variable load rate.

[0054] Optionally, S102 specifically includes: determining the constraints of the deep power regulation of the thermal power unit based on the output threshold value of the thermal power unit under conventional peak regulation and the minimum stable combustion limit output threshold value under the operation condition of deep peak regulation without oil injection; determining the constraints of the variable load rate of the thermal power unit based on the automatic gain control performance of the thermal power unit under deep peak regulation.

[0055] Specifically, the operation of thermal power units at different peak-shaving depths can be roughly divided into the following categories according to whether the peak-shaving process is paid or free and whether oil is needed for stable combustion: conventional peak-shaving of the unit, deep peak-shaving without oil addition, and deep peak-shaving with oil addition. Figure 2is a deep adjustment power limit diagram provided by an embodiment of the present invention, wherein P max is the maximum technical output of the unit, P a is the threshold value of the unit's conventional peak-shaving output. Generally, the conventional peak-shaving interval belongs to the mandatory peak-shaving of thermal power units, so it can be used as the paid peak-shaving boundary load. a At this time, the thermal power unit belongs to the range of unconventional peak regulation, that is, paid peak regulation. b P is the minimum stable combustion limit output threshold value of the thermal power unit without oil injection for deep peak regulation, that is, the boundary load of deep peak regulation without oil injection; c It is the technical minimum output threshold value of deep peak regulation of thermal power units, that is, the minimum technical output. Since the cost of fuel injection and stable combustion of thermal power units is relatively high and does not meet the requirements of economic and environmental factors, such working conditions are generally not considered, and only the peak regulation stage without fuel injection under deep peak regulation conditions is considered. Therefore, the deep regulation power P ds The constraint condition is P b ≤P ds ≤P a .

[0056] The grid dispatching department still requires the AGC (Automatic Gain Control) function to be fully operational for deep peak regulation units, and the minimum stable combustion limit output threshold value P for deep peak regulation without oil injection b and the conventional peak load output threshold value P a The AGC performance requirements include: regulation rate, response time, and regulation accuracy. Among them, the regulation rate is the most important. Because the unit operates under low load conditions, the boiler combustion stability deteriorates, the tube wall temperature is uneven and easy to overheat, and the thermal stress caused by rapid load increase and decrease changes greatly. These factors put forward many restrictions on the regulation rate. Generally, the upper and lower limits of the segmented regulation rate assessment value proposed by the power grid dispatching department are used as the variable load rate v AGC Constraints: v i-1 ≤v AGC ≤v i , where i is the deep peak regulation stage.

[0057] Optionally, the constraint conditions for determining the variable load rate of the thermal power unit based on the automatic gain control performance of the thermal power unit under deep peak regulation include:

[0058] Determine the unit type of the thermal power unit; determine the segmented regulation rate of different unit types that meets the automatic gain control (AGC) performance during deep peak regulation; determine the segmented regulation rate corresponding to different unit types as a constraint condition for the variable load rate.

[0059] For example, taking a regional power grid as an example, the AGC regulation rate requirements of thermal power units under deep peak regulation conditions are shown in Table 1.

[0060] Refer to Table 1, which exemplarily divides the deep peak-shaving condition into three stages, namely 50% Pe>P≥40% Pe, 40% Pe>P≥30% P, and 30% Pe>P. Generally speaking, the regulation rate above 50% Pe / min is a conventional peak-shaving stage, which does not belong to the deep peak-shaving stage considered in the embodiment of the present invention, and the regulation rate of 30% Pe / min is prone to unit operation failure, so it is not considered. Different types of units have different peak-shaving rates for different deep peak-shaving stages.

[0061] It should be noted that between 30% Pe / min and 50% Pe / min, each stage can be divided into smaller stages as needed, such as 30% Pe / min to 35% Pe / min, 35% Pe / min to 40% Pe / min, 40% Pe / min to 45% Pe / min, 45% Pe / min to 50% Pe / min, etc., which will not be repeated here.

[0062] Table 1. AGC regulation rate requirements for different units in a certain area under deep peak load conditions

[0063]

[0064] S103, taking the maximum value of profitability as the first optimization target, taking the peak load constraint condition as the constraint, and using a preset optimization solver to solve, to obtain the coal-fired command curve of the thermal power unit.

[0065] Specifically, the preset optimization solver includes one of the following: GAMS (The General Algebraic Modeling System), Yalmip (a modeling toolkit of Matlab), Cplex (digital optimization technology), Gurobi (a new generation of large-scale mathematical programming optimizer).

[0066] In an embodiment of the present invention, GAMS is preferably used to solve the scheduling model to obtain the coal-fired command curve of the thermal power unit at different times with the deep power regulation and variable load rate of the thermal power unit as constraints and the maximization of the operating benefit of the thermal power unit (that is, the maximum value of the above-mentioned profitability) as the objective function (that is, the above-mentioned first optimization goal), and use it as the input condition for the lower-level optimization goal.

[0067] S104, determining the low-load stable combustion capability of the thermal power unit based on the boiler combustion condition of the thermal power unit.

[0068] Specifically, when the boiler of a thermal power unit is under low load, the lower burner will be put into operation first, and other burners will be put into operation in sequence according to the real-time load size. Therefore, when determining the combination of pulverizers, the coal type corresponding to the lower burner should be made to have better ignition and combustion characteristics. The fluctuation amplitude of the furnace negative pressure is positively correlated with the burner load, so the fluctuation of the furnace negative pressure is an important basis for reflecting the stability of coal powder combustion. Based on this, the low-load stable combustion capability of the thermal power unit includes the calorific value of the coal type arranged in the lower burner and the calorific value of the coal powder under the influence of the fluctuation amplitude of the furnace negative pressure.

[0069] S105, determining the output constraint condition of the coal mill based on the safe operation condition of the boiler.

[0070] Optionally, S105 specifically includes: determining the output reduction and increase of the coal mill based on the current operating conditions of the coal mill of the thermal power unit; determining the output constraint of the coal mill based on the rated output value of the coal mill, and the output reduction and increase of the coal mill.

[0071] Specifically, coal mills have their own characteristics, with maximum and minimum outputs, and need to have a certain adjustable range. According to the actual operation conditions on site, when the coal mill is running at full load, although the operating efficiency is high and the coal consumption per unit is low, it is easy to cause coal mill failure. Therefore, in order to ensure the safe and stable operation of the boiler, the actual output of the coal mill is generally slightly lower than the rated output. That is, based on the safe operation conditions of the boiler, the output constraint condition of the coal mill is determined as: B imin +D - ≤B i ≤B imax -D + , where B imin is the minimum output of the i-th coal mill; B imax is the maximum output of the i-th coal mill; D - i and D + i They are the output down adjustment and output up adjustment respectively.

[0072] S106, taking the maximum value of the low-load stable combustion capacity as the second optimization target, taking the output constraint condition as the constraint, taking the coal-fired command curve as the input, and using the preset optimization solver for secondary solution, obtain the optimal output distribution of the coal mill under deep peak regulation.

[0073] In an embodiment of the present invention, GAMS is preferably used to solve the scheduling model, the output constraints of different coal mills are set as constraints, the maximum value of low-load stable combustion capacity is used as the second optimization goal, and the coal combustion command curve of the thermal power unit at different times is used as input for solving, so as to obtain the optimal output distribution of the coal mill under deep peak regulation.

[0074] This application analyzes the deep regulation capability, low-load stable combustion, and coal mill output control effect of thermal power units, and considers the constraints of equipment, fuel, power grid, etc. under deep peak regulation conditions of thermal power units. A coal mill load scheduling optimization method under deep peak regulation of thermal power units containing two levels is used to determine the output combination plan of the coal mill, thereby solving the technical problems of high total power consumption and poor economy when operating in an evenly distributed output mode under deep peak regulation, and achieving the technical effect of reducing the total power consumption of the coal mill under deep peak regulation and improving the economy of deep peak regulation.

[0075] On the basis of the above technical solutions, S101, obtaining the profitability of thermal power units under deep peak regulation specifically includes:

[0076] Obtain the electricity loss income, peak-shaving compensation income and energy consumption cost of the thermal power units operating under deep peak regulation; subtract the energy consumption cost from the sum of the electricity loss income and the peak-shaving compensation income to obtain the profitability of the thermal power units operating under deep peak regulation.

[0077] Specifically, the power loss benefit P qe,t It is the loss caused by the reduction of power generation due to the reduction of load after deep peak regulation. Generally, the power loss income of the power plant will be compensated in other periods of the year. cp,t Based on the peak-shaving auxiliary service market of each regional power grid, and segmented processing according to the specified peak-shaving depth, for example, a provincial power production enterprise mainly participates in the deep peak-shaving service market in two forms, namely deep peak-shaving transaction and start-stop peak-shaving transaction. Its quotation and settlement methods are usually realized by day-ahead quotation, day-ahead pre-clearing, and intra-day sequential call. Based on this, the peak-shaving compensation income P can be calculated. cp,t Therefore, the maximum profitability of thermal power units operating under deep peak load regulation is Among them, T is time and t is a certain moment.

[0078] Optionally, the energy cost C ec,t It at least includes: coal consumption cost, pollutant emission cost, low-load operation life loss cost, power plant fixed cost and production cost.

[0079] Specifically, the fixed costs of power plants generally refer to a relatively stable part of costs that do not change with the increase or decrease of the load rate. Most of these costs refer to indirect costs and have little to do with production, including depreciation of assets and equipment, personnel salaries, and maintenance costs of non-production equipment. Production costs include fuel costs, maintenance costs, chemicals, water production, limestone, liquid ammonia, etc. Compared with fixed costs, the production cost per unit of power generation, especially the fuel cost, is directly linked to the energy consumption level of the unit. At the same time, in the total production cost, the fuel cost accounts for the highest proportion and the proportion is relatively fixed. In addition, when power plants respond to grid dispatch and participate in deep peak regulation, because the units operate under lower load conditions, they will face challenges such as reduced boiler and turbine efficiency, poor boiler combustion stability, and thermal stress caused by rapid load increases and decreases. The equipment maintenance cost will also increase.

[0080] On the basis of the above technical solutions, S104, determining the low-load stable combustion capability of the thermal power unit based on the boiler combustion condition of the thermal power unit specifically includes:

[0081] The first calorific value of the lower burner is determined based on the type of coal currently burning in the lower burner of the thermal power unit; the second calorific value of the pulverized coal combustion is determined based on the relationship between the fluctuation amplitude of the furnace negative pressure of the thermal power unit and the load of the lower burner; the first calorific value and the second calorific value are added together to obtain the low-load stable combustion capability of the thermal power unit.

[0082] Specifically, to ensure the ignition performance of pulverized coal, the lower layer of the boiler of the thermal power unit should be equipped with coal with high volatility, and in order to minimize the number of coal mills put into operation at low load, the lower layer should be equipped with coal with high calorific value. Since the calorific value of coal shows a parabolic trend of increasing first and then decreasing with its volatility, it is necessary to compromise between volatile matter and calorific value, and to assign different weights to volatile matter and calorific value according to the user's attention to different indicators. The heat generated by this part is the first calorific value mentioned above, expressed as WR QV,t express.

[0083] When the pulverized coal is burning in the boiler, when the combustion is stable, the negative pressure fluctuation in the furnace is small; when the combustion is unstable, the pressure in the furnace fluctuates violently due to continuous explosions and other situations when the pulverized coal is burning in the furnace. The heat generated by this part is the second calorific value mentioned above, which is expressed as WR IDF,t express.

[0084] Therefore, the maximum value of the low-load stable combustion capability of the thermal power unit is

[0086] Figure 3 It is a structural diagram of a coal mill load scheduling optimization device provided by the present invention.

[0087] like Figure 3 As shown, the coal mill load scheduling optimization device specifically includes:

[0088] The revenue acquisition unit 31 is used to acquire the revenue capability of the thermal power unit under deep peak regulation;

[0089] The first condition determination unit 32 is used to determine the peak-shaving constraint conditions of the thermal power unit under deep peak-shaving operation based on the operation conditions of the thermal power unit under different peak-shaving depths, wherein the peak-shaving constraint conditions at least include deep power regulation and variable load rate;

[0090] The first model solving unit 33 is used to take the maximum value of the profitability as the first optimization target, take the peak load constraint condition as the constraint, and use the preset optimization solver to solve and obtain the coal burning command curve of the thermal power unit;

[0091] A stable combustion capability determination unit 34, configured to determine the low-load stable combustion capability of the thermal power unit based on the combustion conditions of the boiler of the thermal power unit;

[0092] A second condition determination unit 35, used to determine the output constraint condition of the coal mill based on the boiler safety operation condition;

[0093] The second model solving unit 36 ​​is used to take the maximum value of the low-load stable combustion capability as the second optimization target, take the output constraint condition as the constraint, take the coal command curve as the input, and use the preset optimization solver to perform a secondary solution to obtain the optimal output distribution of the coal mill under deep peak regulation.

[0094] Optionally, the revenue obtaining unit 31 is specifically used for:

[0095] Obtain the electricity loss income, peak load compensation income and energy consumption cost of thermal power units operating under deep peak load regulation;

[0096] The profit capacity of thermal power units operating under deep peak regulation can be obtained by subtracting the energy consumption cost from the sum of the electricity loss income and the peak regulation compensation income.

[0097] Optionally, the first condition determination unit 32 is specifically configured to:

[0098] Determine the constraints of deep power regulation of thermal power units based on the output threshold value of thermal power units under conventional peak regulation and the minimum stable combustion limit output threshold value under deep peak regulation without oil injection;

[0099] The constraints of the variable load rate of the thermal power unit are determined based on the automatic gain control performance of the thermal power unit under deep peak regulation.

[0100] Optionally, the combustion stabilization capability determination unit 34 is specifically configured to:

[0101] Determine the first calorific value of the lower burner based on the current burning coal type in the lower burner of the thermal power unit;

[0102] Based on the relationship between the fluctuation amplitude of the furnace negative pressure of the thermal power unit and the load of the lower burner, the second calorific value of the pulverized coal combustion is determined;

[0103] The low-load stable combustion capability of the thermal power unit is obtained by adding the first calorific value and the second calorific value.

[0104] Optionally, the second condition determination unit 35 is specifically configured to:

[0105] Determine the output reduction and increase of the coal mill based on the current operation status of the coal mill of the thermal power unit;

[0106] The output constraint conditions of the coal mill are determined based on the rated output value of the coal mill, as well as the output reduction and increase amounts of the coal mill.

[0107] Optionally, the first condition determination unit 32 is further specifically configured to:

[0108] Determine the unit type of thermal power unit;

[0109] Determine the segmented regulation rate that meets the automatic gain control performance for different types of units during deep peak regulation;

[0110] The segmented regulation rates corresponding to different unit types are determined as the constraints of the variable load rate.

[0111] The coal mill load scheduling optimization method provided in the embodiment of the present invention has the same technical features as the coal mill load scheduling optimization device provided in the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0112] Figure 4 It is a structural schematic diagram of a coal mill load scheduling optimization device provided by an embodiment of the present invention. The coal mill load scheduling optimization device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The coal mill load scheduling optimization device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0113] like Figure 4As shown, the coal mill load scheduling optimization device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. Various programs and data required for the operation of the coal mill load scheduling optimization device 10 can also be stored in the RAM 13. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0114] A plurality of components in the coal mill load dispatch optimization device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the coal mill load dispatch optimization device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0115] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a coal mill load scheduling optimization method.

[0116] In some embodiments, the coal mill load scheduling optimization method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the coal mill load scheduling optimization device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the coal mill load scheduling optimization method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the coal mill load scheduling optimization method in any other appropriate manner (for example, by means of firmware).

[0117] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0118] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0119] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0120] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0121] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0122] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0123] An embodiment of the present invention further provides a coal mill, which executes the coal mill load scheduling optimization method in any of the above embodiments.

[0124] The coal mill provided in the embodiment of the present invention uses the coal mill load scheduling optimization method in the above embodiment, so the coal mill provided in the embodiment of the present invention also has the beneficial effects described in the above embodiment, which will not be repeated here.

[0125] In the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0126] Finally, it should be noted that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A coal mill load scheduling optimization method, characterized in that: The optimization method comprises: Obtain the profitability of thermal power units operating under deep peak load regulation; Based on the operation conditions of the thermal power unit at different peak-shaving depths, determining the peak-shaving constraint conditions of the thermal power unit under deep peak-shaving operation, wherein the peak-shaving constraint conditions at least include deep power regulation and variable load rate; Taking the maximum value of the profitability as the first optimization target, taking the peak load constraint condition as the constraint, solving by using a preset optimization solver, and obtaining the coal-fired command curve of the thermal power unit; Determining the low-load stable combustion capability of the thermal power unit based on the boiler combustion condition of the thermal power unit; Determine the output constraint conditions of the coal mill based on the safe operation conditions of the boiler; The maximum value of the low-load stable combustion capability is taken as the second optimization target, the output constraint condition is taken as the constraint, the coal-fired command curve is taken as the input, and the preset optimization solver is used for secondary solution to obtain the optimal output distribution of the coal mill under deep peak regulation.

2. The coal mill load scheduling optimization method according to claim 1, characterized in that: The profitability of thermal power units operating under deep peak load regulation includes: Obtain the electricity loss income, peak load compensation income and energy consumption cost of thermal power units operating under deep peak load regulation; The profit capacity of the thermal power unit under deep peak regulation is obtained by subtracting the energy consumption cost from the sum of the electricity loss income and the peak regulation compensation income.

3. The coal mill load scheduling optimization method according to claim 1, characterized in that: Based on the operation conditions of the thermal power unit at different peak load regulation depths, determining the peak load regulation constraint conditions of the thermal power unit under deep peak load regulation operation includes: Determine the constraint condition of deep power regulation of the thermal power unit based on the output threshold value of the thermal power unit under conventional peak regulation and the minimum stable combustion limit output threshold value under the operation condition of deep peak regulation without oil injection; The constraint conditions of the variable load rate of the thermal power unit are determined based on the automatic gain control performance of the thermal power unit under deep peak regulation.

4. The coal mill load scheduling optimization method according to claim 1, characterized in that: Determining the low-load stable combustion capability of the thermal power unit based on the boiler combustion condition of the thermal power unit includes: Determining a first calorific value of the lower burner based on the type of coal currently burning in the lower burner of the thermal power unit; Determining the second calorific value of the pulverized coal combustion based on the relationship between the fluctuation amplitude of the furnace negative pressure of the thermal power unit and the load of the lower burner; The first calorific value and the second calorific value are added together to obtain the low-load stable combustion capability of the thermal power unit.

5. The coal mill load scheduling optimization method according to claim 1, characterized in that: The output constraints of the coal mill determined based on the safe operation conditions of the boiler include: Determine the output reduction amount and output increase amount of the coal mill based on the current operation status of the coal mill of the thermal power unit; The output constraint condition of the coal mill is determined based on the rated output value of the coal mill, and the output reduction amount and the output increase amount of the coal mill.

6. The coal mill load scheduling optimization method according to claim 3, characterized in that: The constraint conditions for determining the variable load rate of the thermal power unit based on the automatic gain control performance of the thermal power unit under deep peak regulation include: Determining the unit type of the thermal power unit; Determine the segmented adjustment rate that satisfies the automatic gain control performance for different types of units during deep peak regulation; The segmented regulation rates corresponding to the different types of units are determined as constraints on the variable load rate.

7. The coal mill load scheduling optimization method according to claim 2, characterized in that: The energy consumption cost includes at least: coal consumption cost, pollutant emission cost, low load operation life loss cost, power plant fixed cost and production cost.

8. A coal mill load scheduling optimization device, characterized in that: The optimization device comprises: The revenue acquisition unit is used to obtain the revenue capacity of the thermal power unit under deep peak regulation; A first condition determination unit is used to determine the peak-shaving constraint conditions of the thermal power unit under deep peak-shaving operation based on the operation conditions of the thermal power unit under different peak-shaving depths, wherein the peak-shaving constraint conditions at least include deep power regulation and variable load rate; A first model solving unit is used to take the maximum value of the profitability as a first optimization target, take the peak load constraint condition as a constraint, and use a preset optimization solver to solve, so as to obtain a coal-fired command curve of the thermal power unit; a stable combustion capability determination unit, configured to determine the low-load stable combustion capability of the thermal power unit based on the combustion condition of the boiler of the thermal power unit; A second condition determination unit, used to determine the output constraint condition of the coal mill based on the boiler safety operation condition; The second model solving unit is used to take the maximum value of the low-load stable combustion capability as the second optimization target, take the output constraint condition as the constraint, take the coal-fired command curve as the input, and use the preset optimization solver to perform a secondary solution to obtain the optimal output distribution of the coal mill under deep peak regulation.

9. A coal mill load scheduling optimization device, characterized in that: The optimization device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the coal mill load scheduling optimization method described in any one of claims 1-7.

10. A coal mill, characterized in that: The coal mill executes the coal mill load scheduling optimization method described in any one of claims 1 to 7.

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